TY - GEN
T1 - SEMG-based estimation of human arm force using regression model
AU - Wang, Chenliang
AU - Jiang, Li
AU - Guo, Chuangqiang
AU - Huang, Qi
AU - Yang, Bin
AU - Liu, Hong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The force estimation of human arm is one of the main problems while controlling biomechantronic system. It's an effective method to estimate the endpoint force of human arm through the surface electromyography (sEMG) produced by the contraction of the muscles. In this paper, sEMG-based force is estimated by both Bayesian Linear Regression (BLR) and Support Vector Regression (SVR) algorithms. Experimental results show that both models have high performances, with the average of RMS errors in BLR all below 2 N and in SVR mostly below 1 N. Meanwhile, the force estimated by the BLR model is highly linear correlated with the measured value. This paper also discusses the effect of force estimation when electrodes placed in different positions on arm. The performance is better while electrodes are placed on both forearm and upper arm.
AB - The force estimation of human arm is one of the main problems while controlling biomechantronic system. It's an effective method to estimate the endpoint force of human arm through the surface electromyography (sEMG) produced by the contraction of the muscles. In this paper, sEMG-based force is estimated by both Bayesian Linear Regression (BLR) and Support Vector Regression (SVR) algorithms. Experimental results show that both models have high performances, with the average of RMS errors in BLR all below 2 N and in SVR mostly below 1 N. Meanwhile, the force estimated by the BLR model is highly linear correlated with the measured value. This paper also discusses the effect of force estimation when electrodes placed in different positions on arm. The performance is better while electrodes are placed on both forearm and upper arm.
KW - Bayesian Linear Regression
KW - Support Vector Regression
KW - force estimation
KW - surface electromyography
UR - https://www.scopus.com/pages/publications/85049921195
U2 - 10.1109/ROBIO.2017.8324555
DO - 10.1109/ROBIO.2017.8324555
M3 - 会议稿件
AN - SCOPUS:85049921195
T3 - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
BT - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Y2 - 5 December 2017 through 8 December 2017
ER -